Automation has been at the forefront of the digital
revolution for decades, primarily because it maximizes efficiency, reduces
costs and accelerates service levels. But the cloud, mobile and other
innovative technologies – coupled with an ever-growing volume of raw data – have
led to dramatically more complex IT environments.
According to ESG’s
IT Spending Intentions Survey from 2018, 68% of those surveyed said their
IT infrastructures are significantly more complex than they were just two years
ago. Furthermore, 39% of respondents listed automated IT operations as a
critical component of survival in today’s digital age.
In response to this increasing complexity, organizations are
beginning to make the shift toward the next generation of automation – from basic
to intelligent. This new level of automation involves technologies like machine
learning and artificial intelligence to orchestrate workflows across a
multitude of tools, systems and processes.
In fact, with the right platform, it is now possible to
fully automate L2 and L3 tasks – functions which have traditionally required
the use of human judgment. Now, those insights lie within the data itself and
can be extracted, interpreted and leveraged autonomously by AI.
Embracing intelligent
process automation is also enabling enterprises to lay the foundation for AIOps,
a focus area that experts predict will boom over the next five years or so.
AI and ML: Augmenting
IT Operations
AIOps is helping IT teams manage the increasing challenges
created by data and digital disruption, leveraging intelligent process automation
and orchestration to gain competitive advantage. Thanks to the powerful
processing capabilities of artificial intelligence, IT can sort through
mind-boggling amounts of data points to find the proverbial needle in a
haystack.
The role of humans in this increasingly tech-driven
environment is still present, though it too is evolving. Rather than relying on
error-prone employees to handle the bulk of the processing work, human
cognition and advanced skillsets are being used to define that proverbial
needle.
Autonomous operations (AO) utilizes advanced AI to deliver
unassisted responses to IT incidents across the entire infrastructure. Thanks
to the self-learning capabilities of ML algorithms, AO is able to continuously
improve its ability to identify patterns and carry out the appropriate actions.
Again, human workers are still needed in an AO-driven
environment, but in the role of supervisor as opposed to operator. Yet as the
software continues to evolve and improve, and as errors consistently decrease
over time, full autonomy and a zero-touch IT operations environment will one
day become a very real possibility.
The Role of Data
The key to success with intelligent automation is accurate
data, as this enables users to write more impactful rules. There is little to
no value in static data. These days, it’s all about dynamic information which
comes from things like descriptive metadata as well as relational and
behavioral data.
In order to harness this dynamic data and gain adequate insights
from it, organizations need to develop software-defined IT environments. Intelligent
process automation is about the ability to not only proactively identify anomalies,
but to also remediate those issues automatically without causing any business
disruption.
The Right Way to
Automate Intelligently
In today’s competitive landscape, automation is no longer an
option but a necessity. That said, there’s a right way and a wrong way to leverage
this game-changing technology. Start by weighing the time, effort, complexity
and frequency of a given task and then benchmarking these factors against the
cost of transitioning that task to intelligent process automation. From there,
create a prioritized list. This will help you maximize ROI and harness the full
potential of intelligent IT operations.
Not sure where to start? Why not give intelligent process automation a
test drive free for 30 full days? Click here
to launch your Ayehu trial today.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/The-Secret-to-Surviving-the-Tech-Led-Revolution.jpg400626Michal Itzhakihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngMichal Itzhaki2019-11-26 15:48:372019-11-26 15:48:40The Secret to Surviving the Tech-Led Revolution
In today’s increasingly complex digital environment, the
ability to pinpoint, resolve and mitigate potential IT problems has never been
more critical. And with a hybrid blend of public and private cloud, on-premises
and virtual servers, a growing variety of mobile devices and a skyrocketing
volume of network and application traffic, it’s also never been more
challenging. To address this significant concern, organizations are turning to artificial
intelligence for IT operations – or AIOps for short.
The term AIOps encompasses the use of advanced data analytics
technologies, such as AI and machine learning, to automate the process of identifying
and remediating performance issues. AIOps leverages the colossal volume of data
generated by IT services and systems to proactively monitor the infrastructure
and gain complete visibility over all system and application dependencies.
These advanced capabilities enable AIOps to manage and address potential
problems, often before they occur.
Organizations put AIOps in place to gather and analyze all
IT operational data and simultaneously automate all main IT operations. The
AIOps system then organizes and prioritizes that data, presenting it to IT
managers so they can react accordingly. In short, AIOps provides IT
decision-makers with the insight they need to stay a step ahead of IT
operations. Gartner
predicts that by 2023, the use of AIOps will increase from 5% to 30%.
The Key is Automation
The most critical component to a smooth and efficiently run
AIOps is automation. This technology helps AIOps to perform ongoing monitoring
while adhering to predetermined policies and dependency mapping and quickly and
effectively carry out the steps necessary to resolve events or failures.
With all of these technologies operating in tandem, and
automation at the center, AIOps can ultimately help to reduce the volume of
potentially damaging events, provide proactive alerts to issues that could
cause an outage, pinpoint the root cause of those issues and apply intelligent
process automation to autonomously remediate.
AIOps is capable of increasing the effectiveness of
infrastructure resources, streamlining and expediting service requests and
problem resolution, and ultimately generating consistent, measurable value from
its ability to support current and future business initiatives.
The Benefits of AIOps
Harnessing the power of automation in combination with AIOps
delivers a multitude of benefits for IT. Firstly, it can dramatically enhance
and improve the effectiveness of existing tools and services. And since it
saves time while also increasing efficiency and productivity, organizations
employing AIOps can also realize a decrease in overall expenditure.
Likewise, AIOps can also reduce the amount of time and
effort currently required to manage service requests and remediate performance
issues and outages. All of this adds up to improved service levels, a significant
reduction in risk, and a quicker time-to-market for new initiatives.
Automated AIOps runs on a 3-phrase approach:
Identify
Analyze
Respond
In other words, it monitors the environment to detect any
potential anomalies or concerns, then analyzes, validates and prioritizes those
potential events before finally determining the best course of action to take
to address the issue at hand. While this last step may involve escalation to a
human decision-maker, in most cases, these steps can all be carried out without
the need for human intervention. Therein lies the true value of AIOps.
To learn firsthand
how AIOps can help position your organization for future stability and
sustainable success, try it yourself for 30 days. Click here
to start your full-feature trial of Ayehu NG today.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/automation-levels-up-AIOps.jpg417626Michal Itzhakihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngMichal Itzhaki2019-11-04 19:39:532019-11-04 19:39:55How Automation Levels Up AIOps
The
ability to proactively predict and
remediate IT incidents BEFORE they occur, rather than react to them after
they’ve already happened, is one of the key value propositions of a new IT
operations category called AIOps, which stands for Artificial Intelligence
for IT Operations.
Leveraging
the AI part of AIOps to mitigate problems before they become problems is a game
changer for IT. So we’ve partnered with Loom Systems, who like ourselves are a
Gartner Cool Vendor in their category, to demonstrate how two best-of-breed
providers can integrate their respective platforms to create an
enterprise-grade AIOps solution. In doing so, we believe the result is an early
glimpse at the self-healing data center of tomorrow, and we think you’ll be
intrigued to experience how you can peek over the horizon to see and automatically remediate incidents before
they impact end-users.
Let’s
start with the obvious question many of you might have on your mind – what is
AIOps? It is after all, a term that kind of snuck up on all of us.
The term
AIOps, like a lot of buzzwords in our industry, was originated by Gartner. In
this case, a Sr. Director Analyst named Colin Fletcher coined it in 2016,
and its earliest published appearance (as best I can tell) was in early 2017.
Interestingly
though, Colin told me he originally meant the term to refer to Algorithmic IT Operations.
Since then
it’s evolved to refer to Artificial
Intelligence for IT Operations.
Now we all
know how it is in IT marketing. New buzzwords are used to refresh a category and
create excitement. So is AIOps basically just a recycling of the term “IT
monitoring”? Are IT monitoring and AIOps basically the same? Twins, so to
speak, but with different names?
Here’s the
definition for IT Monitoring, courtesy of an internet publication many of you
are probably aware of called TechTarget:
“IT
monitoring is the process to gather metrics about the operations of an IT
environment’s hardware and software to ensure everything functions as
expected to support applications and services.
Basic
monitoring is performed through device operation checks, while more advanced
monitoring gives granular views on operational statuses, including average
response times, number of application instances, error and request rates, CPU
usage and application availability.”
The
operative words there are “gather metrics” – “through device operation checks”.
This
reflects one of the primary characteristics of IT Monitoring – namely that it’s
passive in nature.
And here’s
Colin Fletcher’s original definition for AIOps:
“AIOps platforms
utilize big data, modern machine learning and other advanced analytics
technologies to directly and indirectly enhance IT operations (monitoring,
automation and service desk) functions with proactive, personal and dynamic
insight. AIOps platforms enable the concurrent use of multiple data sources,
data collection methods, analytical (real-time and deep) technologies, and
presentation technologies.”
Unlike IT
Monitoring, AIOps is proactive and far more sophisticated. So AIOps is a LOT
MORE than just IT Monitoring.
At this
point you may be asking yourself, “OK, but how can this benefit me?”
As we all
know, in today’s Digital Era, most businesses are digital or undergoing a
digital transformation, which means that IT systems are replacing many traditional
physical business processes, and that in turn means more work for IT Operations.
In fact, IT
Operations engineers have become responsible for the customers’ digital
experience. When your organization’s systems are misbehaving, underperforming,
or worse not working at all, your customers’ satisfaction is affected, which often
leads to customer churn.
It’s that
simple.
End users
often use applications or websites and love how simple and intuitive they can
be. In IT though, we all know that building something to look nice and simple, can
actually be quite difficult. That’s because there are usually many technologies
under the hood that need to work together seamlessly in order for these digital
experiences to run smoothly.
As if that
wasn’t enough, let’s add some more complexity:
With Cloud
Computing on the one hand, and Microservices architectures on the other, things
become even more complex, for the following reasons:
Cloud computing means abstraction – that can
lead to struggles understanding what the impact of a performance issue on a host
will do to other components of your applications.
These environments change dynamically, making
it harder to stay on top of everything.
Microservices often require disparate data sources,
each generating its own logs and metrics, making tracing and correlation an
inherent part of root cause analysis (RCA).
So, the
increased complexity of digital businesses architectures, coupled with the
explosion of different data types, and the elevated expectations consumers have
these days for seamless end user experiences, makes the life of IT Operations
teams quite challenging.
Enter
AIOps.
AIOps is a
set of tools that enable achievement of optimum availability and performance by
leveraging machine learning technologies against massive data stores with wide
variance. The big idea here is to use machines to deal with machines.
Here are
some examples of the challenges customers often look to address by implementing
AIOps:
Outage prevention – organizations in the
process of cloud migration or architecture change, often look for modern
technologies like AIOps to help them prevent outages before the business is
affected. This is a marked difference from 2 years ago when the market was just
focused on noise reduction. Artificial intelligence and machine learning have
raised expectations of how much more is possible.
Capturing different data feeds – this means
it’s not just about alerts anymore. There’s a huge need to consolidate logs,
metrics, and events together, and to make sense out of them as a whole.
Consolidation of tools – this one is mainly
about the workflow of the users. They’d like AIOps to make their daily lives easier
and consolidate everything into one system.
A
monitoring architecture for modern enterprises that can do all of the above
would be a real-life example of a self-healing architecture.
Everything
starts with observability. Many enterprises use one or more infrastructure
monitoring tools. Application Performance Management (APM) monitors do a great
job in monitoring performance, but are very limited for the application stack
and log management, rendering them a bit unhelpful for triage and forensic investigations.
These
monitoring tools are usually focused on specific data feeds or IT layers, and
they emit alerts when things go wrong. However, these can lead to confusing
alert storms.
This is
another reason why organizations are beginning to leverage AIOps to work for them
and make sense out of it all. Think of AIOps as a robot that turns monotonous
data into information you cannot ignore. In our case, turning logs into
predictions or early stage detection of an outage.
Now that
you know something is about to break, can you prevent it from happening? That’s
exactly the idea of self-healing. When working with an intelligent automation platform
like Ayehu, you can build simple (or complex) remediation workflows, that can
take the alert from Loom Systems and automatically remediate the incident
BEFORE it becomes something more calamitous.
In your
monitoring architecture, you want the Automation tool to seamlessly interact
with both the AIOps solution and your ITSM platform, to open a ticket and
update it as you’re taking remedial action.
When configured
properly, this architecture can resolve issues before they affect the business,
while also documenting what happened for future reference.
Gartner
concurs with this approach.
In a paper
published earlier this year (ID G00384249 – April 24, 2019), they wrote that:
“AI
technologies play an important role in I andO, providing benefits such as
reduced mean time to response (MTTR), faster root cause analysis (RCA) and increased
I andO productivity. AI technologies
enable I andO teams to minimize low-value repetitive tasks and engage in
higher-productivity/value-oriented actions.”
No
ambiguity there.
A little
further down in the same paper, Gartner gave the following recommended actions,
representing their most current advice to infrastructure and operations leaders
regarding AIOps and automation:
“Embark on a journey toward driving
intelligent automation. This involves managing and driving AI
capabilities that are embedded by infrastructure vendors, in addition to
reusing artificial intelligence for operations (AIOps) capabilities to drive
end-to-end (from digital product to infrastructure) automation.”
With AIOps
+ Automation, it’s possible to predict and prevent network outages or other
major disruptions by proactively detecting the conditions leading up to them
and automatically remediating them BEFORE disaster strikes. Given how costly a
service interruption can be to an enterprise, avoiding issues before they
happen will be a critical function in the self-healing data center of tomorrow.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/10/shutterstock_1166070679_website.jpg26244620Guy Nadivihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngGuy Nadivi2019-10-18 13:55:132019-10-18 13:55:15How to Predict and Remediate IT Incidents Before They Affect Business Outcomes [Webinar Recap]
Digital transformation has simultaneously simplified and
added a layer of complexity to the modern world of IT operations. Managing
multiple environments across a number of locations invoked the need to introduce
several disparate tools and platforms, leaving IT siloed and, oftentimes,
overwhelmed. This has perpetuated the need for artificial
intelligence for IT operations, or AIOps for short. For those not yet leveraging
AIOps, or who are still in the beginning stages, here are three real-world,
value-added use cases to consider.
Threat Detection –
AIOps is the perfect complement to a security management strategy because its
machine learning algorithms are capable of mining massive amounts of data for
scripts, botnets and other threats or anomalies that could potentially harm a
network. This is especially true for threats that are complex and
sophisticated, which is why it’s such a valuable addition.
Intelligent Alerting
– Today’s ITOps teams are being inundated with alerts of which only a small
portion are actually critical. AIOps can manage these alerts autonomously,
evaluating, identifying core issues, prioritizing and either escalating or
remediating them without the need for human intervention. Imagine trimming that
overflowing inbox of alerts down to just one or two that truly matter.
Capacity Optimization
– Through the use of AI-based statistical analysis, IT operations teams can
optimize application workloads and availability across the entire
infrastructure. This technology is capable of proactively monitoring bandwidth,
utilization, CPU, memory and much more, with the goal of maximizing application
uptime. AIOps can also be used for predictive capacity planning.
Of course, this is really just the beginning. As
environments become increasingly complex and technology options continue to
grow, IT operations teams will find themselves under even more pressure to
deliver maximum business value with minimal downtime. AIOps emerges as the
ideal solution, facilitating infrastructure monitoring and management that is
much faster and far more efficient. It’s no surprise, that IT leaders and other
key decision-makers are starting to take notice.
Today, AIOps is all about threat management, streamlined
alerting and maximizing uptime. Tomorrow, IT automation powered by artificial
intelligence, machine learning and natural language processing technology is
positioned to forge entirely new pathways for innovation and growth. In other
words, the journey has just begun and the future is beaming with possibility.
Want to get in on the ground floor? Grab your free 30-day trial
of Ayehu NG and put the power of AIOps to work for your
organization.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/09/How-is-AIOps-Really-Used-in-IT.jpg325626peter leehttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngpeter lee2019-09-11 15:18:292019-09-11 15:18:31How is AIOps Really Used in IT?
Today’s IT teams are dealing with a growing mountain of data. What’s more, they’re finding themselves having to use a multitude of tools in order to monitor and manage that data. In situations of technical outages, this can make it incredibly difficult and time-intensive to identify and resolve underlying issues. Anyone in business knows that even just a tiny amount of down-time can have a serious and costly impact on the bottom line. And it’s the IT team that bears the brunt of the burden.
Take, for example, the two largest supermarket chains in Australia. Last year, both experienced severe technical issues which forced them to shut down several stores while they worked on fixing the problem. Not only did those companies lose revenue during the shutdown, but they also suffered a serious blow to their reputation. In other words, customers were not happy.
To better and more quickly identify, resolve and prevent outages and other problems, organizations are turning to artificial intelligence for IT operations (AIOps) – the long-term impact of which will be nothing short of transformational.
What is AIOps
In simplest of terms, AIOps combines data science and machine learning functionality to enhance and/or replace the majority of IT operations functions. This includes performance and availability monitoring, event analysis and correlation, ITSM and automation. To put it even more simply, AIOps platforms gather and analyze all of the data produced by IT to extract what’s of value and present meaningful insights.
How to Get Started with AIOps
Step 1: Don’t put it on the back burner.
If you really want to reap the benefits of AI for your IT operations, the time to jump on the AIOps bandwagon is now. Don’t make this an afterthought or push it out as some far-off future initiative. Even if the actual deployment isn’t imminent, start preparing yourself and others within your organization by becoming familiar with artificial intelligence and machine learning capabilities today. This way, in the event that priorities shift and you need to implement sooner, you’ll already be a few steps ahead of the game.
Step 2: Be careful when choosing your initial test case.
The concept of AIOps at scale may seem overwhelming, but keep in mind that truly transformative initiatives almost always start small. Focus first on capturing knowledge, testing frequently and iterating as needed. You don’t need to be an expert right out of the gate, and not every project you spearhead will be a resounding success. Just be mindful of what you’re starting with and work your way up from there.
Step 3: Work on developing and demonstrating your proficiency.
If you are leading the AIOps charge in your organization, you’ll inevitably be the go-to subject matter expert, at least initially. It will be up to you to communicate and convey the value of the technology to your colleagues and others in leadership. Wear your role with pride and start assembling a team of others who can champion the cause alongside you. Start by identifying gaps that exist in skills and experience, and then create a plan to address those gaps together.
Step 4: Don’t be afraid to experiment.
There are already many AIOps platforms on the market that are incredibly complex and subsequently cost-prohibitive. As with any tech product or solution, it’s wise do experiment and test the waters. Keep in mind that more features doesn’t necessarily equate to a better product. Your organization may not need all those bells and whistles. If possible, take advantage of product demos and free trials. This will enable you to evaluate AIOps uses and applications specific to your business needs without having to invest too heavily or commit to one particular solution.
Step 5: Expand your vision beyond the IT department.
Data management is a massive component of AIOps. Take a step back and examine your organization. Chances are very high that your existing teams are already skilled in this area and that there are data and analytics tools already present within your organization. Resist the urge to reinvent the wheel and be willing to expand your vision to look beyond the IT department. It could save you tremendous time, effort and money.
Step 6: Standardize whenever possible and modernize wherever it makes sense.
You can prepare your existing infrastructure so that it is capable of supporting an AIOps implementation in the future by developing a consistent automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).
Step 7: Consider build-vs-buy.
Understand that there are a number of variables involved in making a shift to AIOps. Likewise, the platforms available on the market today will continue to evolve, as will the infrastructure and applications for which you are responsible currently. Be mindful of this as you weigh whether to purchase a solution or build one of your own. Ideally, the best answer will likely be a combination of the two, so be prepared to figure out which approach best applies where and by how much.
Over the past few years, AIOps has developed from an emerging category to an IT necessity. Successful companies are beginning to leverage AIOps to automate and improve IT operations by applying machine learning to their data. Furthermore, forward-thinking organizations will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.
If AIOps is on your to-do list (and it certainly should be), the steps outlined above should help you to, at the very least, lay the groundwork so that when the time comes to implement, the process will go faster and much more smoothly.
Why wait? Experience the next generation of IT automation, powered by machine learning and artificial intelligence and get started on the fast track to successful AIOps deployment. Start your free 30 day trial of Ayehu today!
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/05/getting-started-with-AIOps.jpg626626Evelyn Kotlerhttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngEvelyn Kotler2019-05-03 17:13:072019-09-25 13:42:007 Steps for Getting Started with AIOps
In today’s volatile marketplace, businesses in every industry are focusing on cutting costs. Unfortunately, some folks still view IT as an expense and an area in which the metaphorical belt can be tightened. What they don’t realize, and what an increasing number of CIO’s are embracing, is that implementing AIOps can actually result in reduced expenditure overall.
CIO’s that are concentrating on IT as a force of operational automation, integration and control are losing ground to executives who see technology as a business amplifier and a source of innovation. Ongoing advances in technology are now providing forward-thinking CIO’s with a much broader spectrum with which to work in terms of cutting costs across the entire organizational platform.
It has nothing to do with cutting IT capability, but rather finding ways to make IT operations more efficient. This is primarily achieved through intelligent automation, which significantly reduces the time and resources needed to run both routine tasks as well as complex workflows. When these tasks and workflows are automated, IT personnel are freed up to focus on other, more critical matters, thereby improving the overall performance of the department and subsequently the company as a whole.
Another way that CIO’s are leveraging AIOps for the benefit of their organizations is through improvement of incident management and mean time to resolution (MTTR). Critical system errors are costly and can have a significant impact on an organization’s bottom line. AI-powered intelligent automation is allowing businesses to manage incidents and downtime scenarios more efficiently and in a much timelier manner, which means less risk of negative impact, both on the business and on the end user.
AIOps isn’t just becoming a tool for cutting costs, either. It’s also significantly improving business performance, which plays a key role in increasing revenue. According to a recent survey conducted by Gartner, the main focus of CIO’s in the current climate is growth. They want to attract new customers and effectively retain their current ones. Intelligent automation helps to improve service levels, thereby improving the customer experience.
In a time when budgets are at the forefront of every manager’s mind, from the top down to those on the front line, finding areas to improve service and lower expenditure has become a necessity. The concept of AIOps has opened up a number of opportunities for streamlining operations and improving efficiency, which ultimately achieves the goal of reducing costs and boosting enterprise growth. By applying technology as an amplifier to business operations, rather than as simply an individual component, organizations that are embracing artificial intelligence and automation are already reaping the benefits and are poised for ongoing success as we move toward the future.
Ready to join these forward-thinking business leaders? Download your free trial of Ayehu and start building your AIOps strategy today.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/04/Smart-CIOs-know-AIOps-is-the-key-to-maximizing-efficiency.jpg379626Michal Itzhakihttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngMichal Itzhaki2019-04-17 06:42:332019-04-10 21:14:35Smart CIOs know AIOps is the key to maximizing efficiency
Thanks to the forces of digital transformation, IT
operations is undergoing some pretty significant changes. Traditional IT
management techniques are becoming obsolete and an entire restructuring of our
IT ecosystems is underway. In response, IT operations leaders are using artificial
intelligence to help them do their work better, faster and cheaper. Gartner
has coined a term for this fundamental shift. It’s known as Artificial
Intelligence for IT Operations, or AIOps for short.
AIOps addresses the challenges of speed, scale and
complexity that IT leaders are facing in the wake of digital transformation. Here
are five specific factors that are driving forces behind AIOps.
Manual Infrastructure
Management – Today’s IT environments are a mishmash of SaaS integrations,
third party services, mobile, managed and unmanaged cloud and more. Traditional
infrastructure management approaches, like manual tracking and oversight, are
simply not adequate in these dynamic, ever-changing environments.
Increase in Data
Retention Requirements – The volume of events and alerts being generated
through performance monitoring is growing at an exponential rate. Furthermore,
the growing number of APIs, IOT devices, mobile applications and digital and/or
machine users is driving service ticket volumes through the roof. This has made
manual analysis and reporting far too complex and cumbersome.
Demand for Faster
Response Time – The more enterprises digitize their business, the more
quickly infrastructure problems must be addressed. User expectations have
evolved thanks to the consumerization of technology, which is driving the
demand for faster reactions to IT events (whether actual or perceived). This is
compounded when the issue in question affects user experience.
More/Expanding
Computing Power – Given how easy it has become to adopt cloud
infrastructure and third party services has empowered individual lines of
business (LOB) to develop their own IT applications and solutions. As a result,
both budget and control have moved from the center of IT to the very edges of
the network, driving the rollout of more computing power.
Influence and Power
of Developers – In modern DevOps, programmers are taking more
responsibility for monitoring at the application level, however, responsibility
for the interaction between services, applications and infrastructure, as well
as accountability for the overall health and function of the IT ecosystem still
lies at the feet of core IT. As digital businesses are becoming more complex,
IT Ops is taking on more responsibility.
Digital transformation is something organizations in every
industry and across the entire globe are striving for. AIOps could very well
hold the key to success. Power your AIOps with the right solution. Click
here to download your free 30-day trial of Ayehu today.
https://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/03/5-things-driving-AIOps.jpg375626peter leehttps://342sv54cwf1w32bxz36tm0bv-wpengine.netdna-ssl.com/wp-content/uploads/2019/11/ayehu-logo-300x92.pngpeter lee2019-03-12 16:03:572019-03-12 16:04:005 Things Driving AIOps